Blar i AURA på forfatter "Goodwin, Morten"
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Digitalization of the power business: How to make this work?
Svendsen, Arne Brufladt; Tollefsen, Trond; Gjengedal, Terje; Goodwin, Morten; Antonsen, Stian (Chapter; Peer reviewed, 2018)As a result of the digitalization of the power business in Norway and Europa, a lot of new possibilities and challenges arise. In 2014 an expert committee one outlined a proposal for the future grid company structure in ... -
Distributed Learning Automata-based S-learning scheme for classification
Goodwin, Morten; Yazidi, Anis; Jonassen, Tore Møller (Peer reviewed; Journal article, 2019) -
Distributed learning automata-based scheme for classification using novel pursuit scheme
Goodwin, Morten; Yazidi, Anis (Journal article; Peer reviewed, 2020) -
Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network
Sharma, Jivitesh; Granmo, Ole-Christoffer; Goodwin, Morten (Peer reviewed; Journal article, 2020)In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The ... -
Escape planning in realistic fire scenarios with Ant Colony Optimisation
Goodwin, Morten; Granmo, Ole-Christoffer; Radianti, Jaziar (Journal article; Peer reviewed, 2014)An emergency requiring evacuation is a chaotic event, filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation ... -
Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor
Johannesen, Nils Jakob; Kolhe, Mohan Lal; Goodwin, Morten (Chapter; Peer reviewed, 2022)Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders ... -
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
Meng, Li; Yazidi, Anis; Goodwin, Morten; Engelstad, Paal (Peer reviewed; Journal article, 2022)In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims to incorporate semi-supervised learning into reinforcement ... -
An Exploration of Semi-supervised Text Classification
Lien, Henrik; Biermann, Daniel; Palumbo, Fabrizio; Goodwin, Morten (Communications in Computer and Information Science;1600, Chapter; Peer reviewed, 2022)Good performance in supervised text classification is usually obtained with the use of large amounts of labeled training data. However, obtaining labeled data is often expensive and time-consuming. To overcome these ... -
Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability
Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Goodwin, Morten (Peer reviewed; Journal article, 2021) -
Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
Engen, Martin; Sandø, Erik; Sjølander, Benjamin Lucas Oscar; Arenberg, Simon; Gupta, Rashmi; Goodwin, Morten (Peer reviewed; Journal article, 2021) -
FlashRL: A Reinforcement Learning Platform for Flash Games
Andersen, Per-Arne; Goodwin, Morten; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2017) -
Following the WCAG 2.0 techniques: Experiences from designing a WCAG 2.0 checking tool
Nietzio, Annika; Eibegger, Mandana; Goodwin, Morten; Snaprud, Mikael (Lecture Notes in Computer Science;7382, Chapter; Peer reviewed, 2012)This paper presents a conceptual analysis of how the Web Content Accessibility Guidelines (WCAG) 2.0 and its accompanying documents can be used as a basis for the implementation of an automatic checking tool and the ... -
Hierarchical Object Detection applied to Fish Species
Kalhagen, Espen Stausland; Olsen, Ørjan Langøy; Goodwin, Morten; Gupta, Aditya (Peer reviewed; Journal article, 2022)Gathering information of aquatic life is often based on timeconsuming methods utilizing video feeds. It would be beneficial to capture more information cost-effectively from video feeds. Video based object detection has ... -
Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain and Yield Predictions
Jacobsen, Sigurd Løite; Kvande, Mikkel Andreas (Master thesis, 2022)Agriculture is a critical part of the world’s food production, being a vital aspect of all societies. Procedures need to be adjusted to their specific environment because of their climate and field condition disparity. ... -
Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain Identification and Grain Yield Predictions
Kvande, Mikkel Andreas; Jacobsen, Sigurd Løite (Master thesis, 2022)Agriculture is a critical part of the world's food production, being a vital aspect of all societies. Procedures need to be adjusted to their specific environment because of their climate and field condition disparity. ... -
Identifying unreliable sensors without a knowledge of the ground truth in deceptive environments
Yazidi, Anis; Oommen, John; Goodwin, Morten (Journal article; Peer reviewed, 2017)This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping ... -
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise
Meng, Li; Goodwin, Morten; Yazidi, Anis; Engelstad, Paal (Peer reviewed; Journal article, 2022)Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst them. It ... -
Increasing sample efficiency in deep reinforcement learning using generative environment modelling
Andersen, Per-Arne; Goodwin, Morten; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2020) -
Indoor Space Classification Using Cascaded LSTM
Yadav, Rohan Kumar; Bhattarai, Bimal; Lei, Jiao; Goodwin, Morten; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2020)Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ ... -
Learning Automata-based Misinformation Mitigation via Hawkes Processes
Abouzeid, Ahmed Abdulrahem Othman; Granmo, Ole-Christoffer; Webersik, Christian; Goodwin, Morten (Peer reviewed; Journal article, 2020)Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool ...